| --- | |
| language: Chinese | |
| datasets: CLUECorpusSmall | |
| widget: | |
| - text: "作为电子[MASK]的平台，京东绝对是领先者。如今的刘强[MASK]已经是身价过[MASK]的老板。" | |
| --- | |
| # Chinese BART | |
| ## Model description | |
| This model is pre-trained by [UER-py](https://arxiv.org/abs/1909.05658). | |
| ## How to use | |
| You can use this model directly with a pipeline for text2text generation : | |
| ```python | |
| >>> from transformers import BertTokenizer, BartForConditionalGeneration, Text2TextGenerationPipeline | |
| >>> tokenizer = BertTokenizer.from_pretrained("uer/bart-chinese-4-768-cluecorpussmall") | |
| >>> model = BartForConditionalGeneration.from_pretrained("uer/bart-chinese-4-768-cluecorpussmall") | |
| >>> text2text_generator = Text2TextGenerationPipeline(model, tokenizer) | |
| >>> text2text_generator("中国的首都是[MASK]京", max_length=50, do_sample=False) | |
| [{'generated_text': '中 国 的 首 都 是 北 京'}] | |
| ``` | |
| ## Training data | |
| [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) Common Crawl and some short messages are used as training data. | |
| ## Training procedure | |
| The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 512. | |
| we convert the pre-trained model into Huggingface's format: | |
| ``` | |
| python3 scripts/convert_bart_from_uer_to_huggingface.py --input_model_path cluecorpussmall_bart_medium_seq512_model.bin-250000 \ | |
| --output_model_path pytorch_model.bin \ | |
| --layers_num 4 | |
| ``` | |
| ### BibTeX entry and citation info | |
| ``` | |
| @article{lewis2019bart, | |
| title={Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension}, | |
| author={Lewis, Mike and Liu, Yinhan and Goyal, Naman and Ghazvininejad, Marjan and Mohamed, Abdelrahman and Levy, Omer and Stoyanov, Ves and Zettlemoyer, Luke}, | |
| journal={arXiv preprint arXiv:1910.13461}, | |
| year={2019} | |
| } | |
| @article{zhao2019uer, | |
| title={UER: An Open-Source Toolkit for Pre-training Models}, | |
| author={Zhao, Zhe and Chen, Hui and Zhang, Jinbin and Zhao, Xin and Liu, Tao and Lu, Wei and Chen, Xi and Deng, Haotang and Ju, Qi and Du, Xiaoyong}, | |
| journal={EMNLP-IJCNLP 2019}, | |
| pages={241}, | |
| year={2019} | |
| } | |
| ``` |